Multi-modal interactive experience platform for teenager education

Abstract

The invention discloses a teenager education-oriented multi-mode interactive experience platform, and relates to the technical field of teenager education, interaction with a 5G intranet is carried out through a gigabit Ethernet, and user interaction comprises a 4KVR head display, a touch screen and a voiceprint recognition voice unit; the resource library is divided into four types of historical scenes and historical events and is updated quarterly; the generation module outputs multi-format content by using a GPT-4 lightweight model and verifies the multi-format content by an expert; intelligent adaptation is based on age, cognition and other portrait adaptation contents; a distributed architecture is adopted for storage, and a learning report is analyzed and output; safety comprises double content auditing and data desensitization; operation and maintenance are monitored for 7 * 24 hours, and remote operation and maintenance and automatic updating are supported; the problems that traditional education is single in form, poor in adaptation and insufficient in immersion are solved. Through multi-modal interaction, intelligent cognitive adaptation, safety control and closed-loop optimization, the interest and emotion resonance of teenagers are stimulated, and the attraction and effectiveness of cultural education are improved.

Description

Technical Field

[0001] This invention relates to the field of youth education technology, and in particular to a multimodal interactive experience platform for youth education. Background Technology

[0002] Culture, as a core component of youth education, plays an irreplaceable role in cultivating sentiments and spirit. With the popularization of digital education, traditional education models are increasingly unable to adapt to the cognitive habits and learning needs of young people. Current policies highly value the development of digital education, requiring the transformation of resources towards immersive and interactive educational platforms; however, existing technological solutions still have many shortcomings and have failed to form a systematic solution.

[0003] Traditional education relies heavily on classroom lectures, book reading, and offline visits, resulting in a rigid and one-way output that fails to stimulate active participation among young people. While some digital platforms have achieved online presentation of resources, they often remain at the level of simple text, images, audio, and video compilations, lacking multimodal interactive experiences. Young people can only passively watch and cannot deeply engage with historical scenes to form an emotional connection. The few platforms that have introduced VR / AR technology also suffer from crude interactive design: VR scenes are often based on fixed scripts and cannot be dynamically adjusted according to user feedback; AR integration effects are stiff, the boundary between virtual and reality is blurred, and its applicability is limited to professional exhibition halls, making it difficult to implement in daily school teaching. Furthermore, content presentation fails to consider the differences in cognitive development among young people, using a uniform standard to push content. Younger children lose interest due to overly difficult content, while older children find it too simplistic and lacking in opportunities for reflection.

[0004] Weaknesses in content customization, security control, and system maintenance further hinder the effectiveness of educational implementation. Teachers struggle to customize interactive content based on teaching objectives and class characteristics, and platforms lack flexible story editing and evaluation tools, failing to adapt to personalized teaching needs. As minors, teenagers face insufficient protection of their data privacy and usage security; some platforms fail to implement time limits and content rating systems, posing risks of excessive use of electronic devices or exposure to inappropriate content. At the system level, fluctuations in concurrency frequently lead to lag and crashes, especially during peak usage periods on campus, where server overload causes a decline in user experience, and the lack of intelligent scaling and self-healing capabilities results in high maintenance costs. Furthermore, existing platforms often lack learning outcome feedback mechanisms, making it impossible to optimize content and teaching strategies through data analysis to form a closed loop of "content delivery - learning experience - outcome evaluation."

[0005] These problems make it difficult for cultural education to truly reach the hearts of teenagers, significantly diminishing its effectiveness. For example, complex text interpretations are presented to elementary school students, who struggle to understand them; VR experience programs organized by middle schools, lacking emotional interaction, are seen by students as mere "games" rather than educational processes; and frequent platform glitches during peak campus usage disrupt teaching plans. As Generation Z teenagers become the main force in education, their demands for digitalization, interactivity, and personalization are increasingly strong. There is an urgent need to build a cultural education platform that integrates "immersive interaction, intelligent adaptation, security and controllability, and closed-loop optimization" to overcome current technological bottlenecks and application challenges in education. Summary of the Invention

[0006] The present invention proposes a multimodal interactive experience platform for youth education to solve the problems mentioned in the prior art.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A multimodal interactive experience platform for youth education includes the following modules: The user interaction module includes an immersive interaction unit, a touch interaction unit, and a voice interaction unit. The immersive interaction unit is equipped with a 4K resolution VR headset and force feedback controllers. The touch interaction unit uses a 21.5-inch multi-touch screen. The voice interaction unit supports voiceprint recognition for teenagers aged 3-18, can receive commands in Mandarin Chinese and 20 dialects, and has a voice wake-up function within 100ms.

[0008] The cultural resource library module is divided into historical scene library, historical event library, document library and interactive task library according to content type. The historical scene library contains 3D scene models, the historical event library contains 1,000+ biographies, the document library stores 5,000+ scanned documents, and the interactive task library designs 600+ tiered tasks. The resource library is updated quarterly, and new content is launched after a 3-level review.

[0009] The multimodal content generation module uses the GPT-4 lightweight model and the StableDiffusion image generation engine. After receiving the requirements from the intelligent adaptation module, it generates text interpretations, 3D scene fragments, and interactive scripts that are adapted to the cognition of teenagers. It supports multi-format output of text, images, audio, and VR / AR scenes. The generated content needs to be pre-verified by a cultural expert database.

[0010] The intelligent adaptation module adapts content based on user profiles, which include age, cognitive level, interests and preferences, and interaction habits. Based on the profiles, it calls the corresponding content from the resource library and the generation module, and adjusts the response time and content complexity accordingly.

[0011] The data storage and analysis module adopts a distributed storage architecture to store user interaction logs, profile data, and generated content. The analysis module uses big data algorithms to mine user learning paths, knowledge mastery, and interaction preferences, and outputs weekly youth cultural learning reports to support content optimization and teaching suggestion generation.

[0012] The security management module includes a content security unit and a privacy protection unit. The content security unit adopts a dual mechanism of keyword filtering and AI review. The privacy protection unit complies with the "Regulations on the Protection of Minors on the Internet". User names, portraits and other data are stored in anonymized form. Access permissions are divided according to the "minimum necessary" principle. Parents can query their children's learning data but cannot tamper with it.

[0013] The system operation and maintenance module enables 24 / 7 status monitoring, including hardware status monitoring and software performance monitoring. The fault warning threshold is set to hardware failure rate >1% and response latency >200ms. After a warning, a work order is automatically triggered. Remote operation and maintenance and automatic version updates are supported.

[0014] Furthermore, it includes an emotion perception and adaptation submodule, which integrates physiological sensors and an emotion analysis engine. The physiological sensors adopt a wearable wristband design to collect the user's heart rate, skin conductance, and blood oxygen saturation. The emotion analysis engine combines voice emotion features with facial micro-expression data, according to... The system calculates the real-time emotion matching score for users, where M is the emotion matching score, T is the interaction duration, H(t) is the normalized heart rate value at time t, S(t) is the normalized skin conductance response value at time t, V(t) is the normalized voice emotion value at time t, and O(t) is the normalized facial micro-expression value at time t. a, b, c, and d are the weighting coefficients for heart rate, skin conductance, voice, and facial expression, respectively. By capturing the emotional fluctuations of teenagers in historical scene experiences in real time, the system dynamically adjusts the rhythm and form of content presentation to enhance learning immersion and sustainability.

[0015] Furthermore, it includes an AR scene virtual-real fusion submodule. This submodule uses visual SLAM+IMU tightly coupled positioning technology and deep learning image recognition algorithms to overlay virtual cultural elements onto real-world scenes, supporting multi-device adaptation. Three fusion modes are designed: desktop fusion mode can overlay 3D statues and document replicas onto a desk; mobile fusion mode can overlay dynamic clips of historical events onto open outdoor areas; and textbook fusion mode can scan a page in a history textbook and automatically overlay a 3D map and a character relationship diagram of the corresponding event next to the page. The fusion process enables ambient light adaptation and shadow rendering functions, automatically adjusting the brightness of virtual elements according to the intensity of real-world lighting and generating shadows for virtual elements based on real-world ground textures, with a natural transition between virtual and real boundaries of ≥90%. Interactive triggering and scene editing are supported: explanations can be triggered by gestures such as "wave to wake," "two-finger zoom," and "touch virtual objects." Teachers can customize the position, size, and trigger conditions of virtual elements through a web editor, adapting to common scenes such as primary and secondary school classrooms, school squares, and history museums. Real-world teaching can be conducted without professional venue setup, reducing the cost of building educational scenarios.

[0016] Furthermore, the cultural resource library module also includes an interactive storyline customization sub-module. This sub-module provides a visual storyline branching logic editor, allowing teachers to customize the storyline based on the historical framework of the basic resource library. It supports adding conditional task nodes, and each node can be configured with multiple types of interaction. Multimedia material import and editing are supported: teachers can upload local historical photos and audio clips as storyline clues, add custom narration, and set clue prompt levels. A tiered evaluation system is constructed, including process evaluation and outcome evaluation, with customizable evaluation weights. After the customized storyline undergoes system compliance review, a unique QR code and sharing link are generated. Students can access the customized storyline by scanning or clicking the link on the platform. Storyline data is synchronized to the teacher's backend in real time, supporting viewing of individual student progress, overall class completion rate, top 3 error rate knowledge points statistics, and generating class analysis reports. A supporting storyline template library is also provided, allowing teachers to directly reuse templates for quick modification and customization to adapt to personalized and group teaching scenarios.

[0017] Furthermore, the intelligent adaptation module also includes a cognitive development matching submodule. This submodule is based on Piaget's cognitive development theory and the adolescent cultural learning syllabus. It refines cognitive levels into 6 stages and combines them with the user's historical learning data to calculate the cognitive matching coefficient of the content. Here, C is the cognitive matching coefficient, n is the number of historical learning contents, d_i is the difficulty coefficient of the i-th content, f(L_i) is the learning time function of the i-th content (f(L_i)=1 / (1+0.1L_i), where L_i is the learning time in minutes. The longer the time, the smaller the function value, reflecting the matching degree between the content difficulty and the user's acceptance), and g(K_i) is the knowledge mastery function of the i-th content (g(K_i)=1-0.02(100-K_i), where K_i is the test accuracy rate corresponding to the content. The higher the accuracy rate, the larger the function value, reflecting the user's mastery of the content). A dynamic update mechanism for cognitive stages is established, automatically pushing out a cognitive level test every 3 months (containing 10 basic questions and 5 analytical questions, with a test duration of 15 minutes). Based on the test results, the weights of the user's cognitive stage tags and matching coefficients are adjusted (e.g., in the specific calculation stage, the weight of g(K_i) is increased to 0.6). Based on the C value, precise content adaptation is achieved to avoid decreased learning interest caused by content being too difficult or too easy, thus improving knowledge absorption efficiency.

[0018] Furthermore, the multimodal content generation module also includes a voice narrative optimization submodule. This submodule uses a youth-friendly voice synthesis engine based on the Transformer architecture, with 5 built-in basic timbres and 3 style modes, supporting customizable adjustments to speech rate, intonation, and volume. It optimizes voice content generation for the language habits of teenagers: short sentences, common vocabulary, and onomatopoeia are used for younger learners, avoiding obscure terminology; written language and logical connectors are added for older learners, with appropriate introduction of professional expressions. A dialect voice optimization library is built, correcting for the pronunciation characteristics of 20 dialects including Cantonese, Sichuanese, and Shanghainese, achieving a dialect voice recognition accuracy of ≥92% and a synthesis accuracy of ≥95%. It achieves contextualized adaptive voice intonation; switching to a "warm mode" when recounting historical figures' family letters; and switching to a "friendly mode" during question-and-answer interactions. It possesses multi-turn voice dialogue and historical fact correction capabilities: supporting continuous follow-up questions from students, the engine calls upon the cultural knowledge base to generate coherent answers in real time; after recognizing historical errors in the voice narrative, it immediately triggers double verification, with a correction response time ≤500ms, ensuring the accuracy and rigor of cultural knowledge transmission.

[0019] Furthermore, the data storage and analysis module also includes a learning effectiveness visualization sub-module. This sub-module transforms the analysis results into concrete charts and incentive systems that are easy for teenagers to understand. It has three core functions: First, visualization of knowledge mastery, using a dynamic "growth tree" model. Each knowledge point corresponds to a leaf: ≥80% is a "green leaf," 60%-79% is a "yellow leaf," and <60% is a "withered leaf." Clicking on a leaf allows users to view the learning record and supplementary learning content for that knowledge point. Second, interest preference analysis, using an "interest radar chart" to display the intensity of preferences across eight dimensions, including historical events, biographies, military equipment, and document interpretation, and recommending similar content based on preferences. Third, learning trajectory tracking, using a "timeline" to mark key learning nodes, supporting filtering by time to view corresponding content and interaction data. A complementary "achievement badge system" is also included, with various badges awarded for conditions such as "7 consecutive days of learning," "perfect score on a knowledge point test," and "completion of a customized storyline task." Unlocking a badge triggers an animated explanation, enhancing the sense of accomplishment in learning. The design incorporates multiple viewpoints: the student view displays complete visual content and badges; the parent view provides a simplified report, including weekly learning time, key and weak knowledge points, and personalized learning suggestions; and the teacher view provides a class summary view, anonymously displaying class knowledge mastery rankings and common weak knowledge points, and supports exporting PDF reports for school archiving, facilitating collaborative cultural education between home and school.

[0020] Furthermore, the user interaction module also includes a haptic feedback optimization submodule. This submodule is designed for interacting with historical props in VR scenes, utilizing the vibration motor and position sensor of the force feedback controller to provide haptic feedback. The calculation of force feedback intensity is performed, where F is the handle force feedback intensity, k is the scene emotion coefficient, s is the interaction displacement, P(x) is the virtual prop hardness function at displacement x, and T(x) is the interaction time function. A virtual prop material library is constructed, containing P(x) parameter models for 10 common historical prop materials such as metal, wood, paper, cloth, and ceramics, supporting user-defined upper limits for feedback intensity. Haptic and multimodal linkage is implemented: when touching a virtual rifle, the handle provides instantaneous strong vibration, simultaneously triggering voice narration, and the VR screen magnifies to show rifle details; when flipping through a virtual family letter, the handle provides gentle continuous vibration, accompanied by the sound effect of turning pages, and the screen displays magnified text; when holding a virtual red flag, the feedback is a gradual vibration, simultaneously playing the sound effect of the red flag waving. The feedback logic is optimized for younger users, the vibration mode is simplified, haptic guidance functions are added, and the realism and guidance of the interaction are enhanced, helping teenagers to more intuitively understand the characteristics and usage scenarios of objects in historical scenes.

[0021] Furthermore, the security management module includes a usage time management sub-module, which supports setting usage rules for multiple roles and is subdivided into basic management and personalized management: basic management includes single usage time limits, daily usage periods, and weekly cumulative time limits; personalized management allows setting permissions based on content type. It combines intelligent reminders with mandatory control: when the remaining time is ≤5 minutes, a dual voice reminder is issued; after the timeout, interactive functions are automatically locked, requiring parental fingerprint verification or teacher password to unlock. It adds usage status monitoring and health advice: the VR headset camera monitors the user's posture, issuing reminders when "slouching" is detected; it tracks continuous usage time, forcibly popping up a 5-minute break screen every 40 minutes, playing eye exercise animations or historical knowledge quizzes. A hierarchical permission system is designed: parents can view all their children's usage data and modify management rules; homeroom teachers can only view the usage time and learning progress of students in their class and modify unified class management rules; school administrators can view school-wide data and set general management templates, complying with the requirements of the "Regulations on the Protection of Minors Online" and fulfilling the responsibility for managing minors' online use.

[0022] Furthermore, the system operation and maintenance module also includes a load balancing and intelligent scaling submodule. This submodule adopts a distributed node architecture, monitoring the CPU utilization, memory usage, disk I / O rate, and concurrent connections of each node in real time. It allocates user requests through an improved round-robin algorithm, achieving a request response time of ≤100ms. It implements predictive load scheduling and intelligent scaling: based on historical data, a load prediction model is built, switching idle nodes to active status 10 minutes in advance. When the predicted concurrency exceeds 120% of the current node's capacity, an elastic cloud node is automatically activated. During peak periods, a traffic shaping mechanism is used to prioritize interactive requests and delay non-core requests. It features fault self-healing and data consistency assurance: a heartbeat detection mechanism is used; when a node failure is detected, user requests for that node are automatically migrated to a backup node, while simultaneously triggering data backup. Distributed locks and data synchronization protocols ensure multi-node data consistency, preventing progress loss or data corruption caused by request migration. The system is designed with an energy-saving operation and maintenance mode: During off-peak periods, 50% of core nodes and all standby nodes are automatically shut down, and a sleep / wake-up mechanism is enabled; node resource utilization is monitored, and nodes with utilization rates below 30% for one consecutive hour are run at reduced frequencies to lower system energy consumption. A supporting visual operation and maintenance backend displays node status, load distribution, fault statistics, and expansion records, supports remote operation and maintenance, and boasts a fault repair success rate of ≥90%. Version updates utilize a canary release model to ensure stable 24 / 7 platform operation and meet the needs of a large number of teenagers using the platform simultaneously.

[0023] Compared with existing technologies, the beneficial effects of this invention are: This invention addresses the core pain points of traditional education—namely, its "single format, insufficient adaptability, rigid experience, and lack of control"—through multi-module collaborative innovation and technical solution optimization. It constructs a new, high-quality cultural education platform that significantly enhances the attractiveness and effectiveness of education.

[0024] The upgraded multimodal interactive experience enables immersive cultural transmission. The user interaction module integrates VR, touch, voice, and other multi-interaction units, along with a haptic feedback optimization sub-module, simulating the tactile differences of historical props and the atmosphere of the scene, transforming teenagers from "observers" into "participants." The AR scene virtual-real fusion sub-module breaks through scene limitations, naturally blending virtual elements with everyday scenes such as the campus and desks, allowing for real-world teaching without the need for professional venues, thus lowering the threshold for educational implementation. The emotional perception and adaptation sub-module captures physiological and emotional signals such as heart rate and facial expressions, dynamically adjusting the content rhythm and presentation format. It inserts interpretive content when teenagers are confused and extends immersion time when they feel awe, effectively stimulating emotional resonance and elevating culture from knowledge transmission to emotional identification.

[0025] The enhanced capabilities of intelligent adaptation and content innovation meet the diverse educational needs. The intelligent adaptation module, based on cognitive development theory, constructs a matching model to accurately push content according to the age and cognitive level of teenagers. For younger children, it emphasizes engaging interactions and illustrated stories, while for older children, it adds logical analysis and textual interpretation, avoiding mismatches between content and cognitive understanding. The interactive story customization sub-module provides teachers with visual editing tools, supporting the customization of story branches, task formats, and evaluation criteria, adapting to personalized and group teaching scenarios. A template library is also included to lower the barrier to entry. The multimodal content generation module, through features such as optimized voice narration and historical fact correction, ensures that the content conforms to the language habits of teenagers while maintaining the accuracy and rigor of knowledge, balancing educational value with entertainment.

[0026] The robust security management and system operation and maintenance mechanisms provide a reliable guarantee for the effective implementation of education. The usage time control sub-module of the security management module implements the requirements for the protection of minors, supports multi-role usage rule settings, and combines intelligent reminders and health suggestions to balance educational needs with eye health. The privacy protection mechanism performs anonymized storage and hierarchical access control for user data, eliminating the risk of information leakage. The load balancing and intelligent scaling sub-modules of the system operation and maintenance module handle concurrency fluctuations through predictive scheduling and elastic scaling, maintaining a smooth experience during peak usage periods on campus, and its self-healing capabilities reduce operation and maintenance costs and teaching interruptions. The learning effectiveness visualization sub-module transforms analysis results into tangible forms such as growth trees and badges, combined with home-school collaborative reports, making learning effectiveness perceptible and measurable, while providing data support for content optimization, forming a closed-loop education system.

[0027] Overall, this invention constructs a cultural education technology system of "immersive interaction, intelligent adaptation, security and controllability, and closed-loop optimization," effectively solving the problems of "difficulty in getting people to understand, difficult to implement, and difficult to evaluate" in traditional education. It allows culture to be transmitted in a way that young people enjoy, enhancing their learning interest and participation while deepening their emotional development, and providing strong support for the digital development of youth education. Attached Figure Description

[0028] Figure 1 This is a schematic block diagram of the multimodal interactive experience platform for youth education proposed in this invention; Figure 2 Comparison chart of bytecode enhancement coverage; Figure 3 A pie chart showing the weight distribution of risk assessment dimensions; Figure 4 This is a comparison chart of the effects of exception handling; Figure 5 A line chart comparing performance overhead; Figure 6 A comparison chart showing the teaching efficiency and adaptation effect of customized sub-modules for interactive storylines. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0031] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0032] Reference Figures 1 to 5 A multimodal interactive experience platform for youth education, comprising the following modules: The user interaction module includes an immersive interaction unit, a touch interaction unit, and a voice interaction unit. The immersive interaction unit is equipped with a 4K resolution VR headset (110° field of view, 90Hz refresh rate) and force feedback controllers (vibration intensity adjustable from 0-100 levels). The touch interaction unit uses a 21.5-inch multi-touch screen (response time ≤50ms). The voice interaction unit supports voiceprint recognition for teenagers aged 3-18 (accuracy ≥95%), can receive Mandarin Chinese and 20 dialect commands, and has a voice wake-up function within 100ms.

[0033] The cultural resource library module is divided into four categories based on content type: historical scene library, historical deeds library, document library, and interactive task library. The historical scene library contains 3D scene models (resolution ≥ 1 million polygons), the heroic deeds library contains 1,000+ biographies (including text, images, audio and video), the document library stores 5,000+ scanned documents (resolution 300 DPI), and the interactive task library designs 600+ graded tasks (suitable for different age groups from 6 to 18 years old). The resource library is updated quarterly, and new content is launched after a 3-level review process.

[0034] The multimodal content generation module uses the GPT-4 lightweight model and the StableDiffusion image generation engine. After receiving the requirements from the intelligent adaptation module, it generates text interpretations adapted to the cognition of teenagers (vocabulary difficulty corresponds to the compulsory education curriculum standards), 3D scene fragments (rendering frame rate ≥60fps), and interactive scripts (containing ≥3 plot branches). It supports multi-format output of text, images, audio, and VR / AR scenes. The generated content must be pre-verified by a cultural expert database (50+ domain experts).

[0035] The intelligent adaptation module adapts content based on user profiles. These profiles include age (6-9 years, 10-13 years, and 14-18 years), cognitive level (classified as primary, intermediate, and advanced through entrance tests), interests (historical events, biographies, military equipment, etc.), and interaction habits (VR immersion, touch operation, voice Q&A). Based on the profiles, the module calls upon the corresponding content from the resource library and generates the module, adjusting the response time (extended to 150ms for younger users) and content complexity accordingly.

[0036] The data storage and analysis module adopts a distributed storage architecture (capacity ≥100TB, read / write speed ≥500MB / s) to store user interaction logs, profile data, and generated content. The analysis module uses big data algorithms to mine user learning paths (tracking accuracy down to the second), knowledge mastery (calculated through the correctness of interactive quizzes), and interaction preferences, and outputs weekly youth cultural learning reports to support content optimization and teaching suggestion generation.

[0037] The security management module includes a content security unit and a privacy protection unit. The content security unit adopts a dual mechanism of keyword filtering (1000+ sensitive words) and AI review (recognition accuracy ≥99%). The privacy protection unit complies with the "Regulations on the Protection of Minors on the Internet". User names, portraits and other data are stored in anonymized form (using hash encryption algorithm). Access permissions are divided according to the "minimum necessary" principle. Parents can query their children's learning data but cannot tamper with it.

[0038] The system operation and maintenance module enables 24 / 7 status monitoring, including hardware status monitoring (VR device failure rate, network bandwidth utilization) and software performance monitoring (module response time, content loading speed). The fault warning threshold is set to hardware failure rate > 1% and response latency > 200ms. After a warning, a work order is automatically triggered (work order response time ≤ 10 minutes). Remote operation and maintenance (fault repair success rate ≥ 90%) and automatic version updates (executed during non-teaching periods) are supported.

[0039] This invention also includes an emotion perception and adaptation submodule, which integrates physiological sensors and an emotion analysis engine. The physiological sensors adopt a wearable wristband design (weight <50g, battery life ≥8 hours) to collect the user's heart rate (measurement range 60-180 beats / min, accuracy ±1 beat / min), skin conductance (0-5μS, accuracy ±0.1μS), and blood oxygen saturation (90%-100%, accuracy ±1%). The emotion analysis engine combines voice emotion features (pitch frequency 200-3000Hz, speech rate 80-160 words / minute) and facial micro-expression data (collected through a VR headset camera, recognizing 7 basic emotions) and... Calculate the real-time emotion matching degree of the user, where M is the emotion matching degree (value from 0 to 1, the closer to 1 the higher the matching degree), T is the interaction duration (unit: s), H(t) is the normalized value of heart rate at time t (0-1), S(t) is the normalized value of skin conductance response at time t (0-1), V(t) is the normalized value of voice emotion at time t (0-1), O(t) is the normalized value of facial micro-expression at time t (0-1), and a, b, c, and d are the weight coefficients of heart rate, skin conductance, voice, and facial expression, respectively (the sum is 1, 0.3, 0.3, 0.2, and 0.2 for the 6-9 age group, and 0.4, 0.2, 0.3, and 0.1 for the 14-18 age group). By capturing teenagers' emotional fluctuations during their historical scene experiences in real time, the rhythm and form of content presentation are dynamically adjusted: when M < 0.6 and is identified as "confusion", a 30-second animated short film is automatically inserted to interpret the core historical facts; when M ≥ 0.8 and is identified as "awe", the scene immersion time is extended and detailed explanations are triggered; when "fatigue" is identified, interactive mini-games (such as "historical knowledge puzzles") are switched to provide a rest-style learning experience, enhancing the immersion and sustainability of learning.

[0040] This invention also includes an AR scene virtual-real fusion submodule. This submodule uses visual SLAM+IMU tightly coupled positioning technology (positioning accuracy ±5cm, drift error ≤2cm / minute) and deep learning image recognition algorithms (supporting three types of recognition objects: natural scenes, textbook covers, and historical photos, with a recognition speed ≤200ms) to overlay cultural virtual elements onto real-world scenes, supporting multi-device adaptation (smartphones, tablets, AR glasses, touch screen all-in-one machines). Three fusion modes are designed: a desktop fusion mode can overlay 3D character statues (500,000 polygons resolution, supporting 360° rotation viewing) and document replicas (with virtual page turning) onto a desk surface; a mobile fusion mode can overlay dynamic fragments of historical events onto open outdoor areas; and a textbook fusion mode can scan a page of a historical textbook and automatically overlay a 3D map of the corresponding event and a character relationship map next to the page. The fusion process enables ambient light adaptation and shadow rendering functions, automatically adjusting the brightness of virtual elements (0-255cd / m²) according to the real-world light intensity (0-10000lux). 2 Based on real-world ground textures, virtual element shadows are generated with a natural transition between virtual and real boundaries of ≥90%. Interactive triggering and scene editing are supported: explanations can be triggered via gestures such as "waving to wake up," "two-finger zoom," and "touching virtual objects." Teachers can customize the position, size, and trigger conditions of virtual elements through a web-based editor (e.g., setting "walking to the school sculpture to awaken the corresponding historical event"). It adapts to common scenarios such as primary and secondary school classrooms, school squares, and history museums, enabling real-world teaching without the need for professional venue setup, thus reducing the cost of building educational scenarios.

[0041] In this invention, the cultural resource library module also includes an interactive storyline customization submodule. This submodule provides a visual storyline branching logic editor, allowing teachers to customize the storyline based on the historical framework of the basic resource library. It supports adding conditional task nodes (e.g., "Answering questions with a correct rate ≥80% enters branch A, <80% enters branch B (basic historical facts consolidation)"; "Choosing different strategies leads to different task results"). Each node can be configured with multiple interactive formats (answering questions, puzzles, virtual operations, voice Q&A). Multimedia material import and editing are supported: teachers can upload local historical photos and audio clips (e.g., oral history recordings from elderly people) as storyline clues, add custom narration (supporting text-to-speech or audio recording), and set clue prompt levels (0-3 prompts, with prompts gradually unlocking from vague to specific). A tiered evaluation system is constructed, including process evaluation (task participation time, number of clue usages, and interaction accuracy) and outcome evaluation (final knowledge point test score). Evaluation weights can be customized (e.g., process evaluation accounts for 40%, outcome evaluation accounts for 60%). Once the customized storyline is completed, it undergoes a system compliance review (including verification of historical accuracy and content suitability, completed within 10 minutes) to generate a unique QR code and sharing link. Students can access the customized storyline by scanning or clicking the link on the platform. Storyline data is synchronized to the teacher's backend in real time, supporting the viewing of individual student progress (updated once every 30 seconds), overall class completion rate, statistics of the top 3 knowledge points with the highest error rates, and the generation of a class analysis report (including personalized tutoring suggestions). A supporting storyline template library (divided into 6 basic templates by grade level, such as "character short story storylines" for lower grades and "historical event decision-making simulation storylines" for higher grades) is provided. Teachers can directly reuse templates to quickly modify and customize them to suit the needs of personalized and group teaching scenarios.

[0042] In this invention, the intelligent adaptation module also includes a cognitive development matching submodule. This submodule, based on Piaget's theory of cognitive development and the adolescent cultural learning syllabus, refines cognitive levels into six stages (corresponding to the operational stage before age 6-7, the concrete operational stage at age 8-11, and the formal operational stage at age 12-18, with each stage further divided into elementary and intermediate levels). It combines this with the user's historical learning data (interaction records, test scores, and learning time over the past three months) and, according to... Calculate the content cognitive matching coefficient, where C is the cognitive matching coefficient (value 0-1, ≥0.7 is suitable, <0.5 is severely unsuitable), n is the number of historical learning contents, d_i is the difficulty coefficient of the i-th content (0.1-1.0, calculated based on three dimensions: knowledge depth, logical complexity, and interaction intensity, such as "identifying a person's name" d_i=0.2, "analyzing the significance of historical events" d_i=0.9), f(L_i) is the learning time function of the i-th content (f(L_i)=1 / (1+0.1L_i), L_i is the learning time in minutes, the longer the time, the smaller the function value, reflecting the matching degree between content difficulty and user acceptance), g(K_i) is the knowledge mastery function of the i-th content (g(K_i)=1-0.02(100-K_i), K_i is the test accuracy corresponding to the content, the higher the accuracy, the larger the function value, reflecting the user's mastery of the content). A dynamic update mechanism for cognitive stages is established, automatically pushing out a cognitive level test every 3 months (containing 10 basic questions and 5 analytical questions, with a test duration of 15 minutes). The weighting of the user's cognitive stage tags and matching coefficients is adjusted based on the test results (e.g., in the concrete operational stage, the weight of g(K_i) is increased to 0.6). Precise content adaptation is achieved based on the C value: for children under 6-7 years old in the operational stage, "illustrated character stories" and "simple historical scene coloring interactions" are pushed (d_i≤0.3, C≥0.8); for children aged 10-11 years old in the concrete operational stage, "historical event timeline analysis" and "character relationship graph construction" are pushed (d_i0.4-0.6, C≥0.75); for children aged 16-18 years old in the formal operational stage, "document interpretation and viewpoint discussion" and "simulating historical decisions and analyzing their impact" are pushed (d_i0.7-1.0, C≥0.7). This avoids decreased learning interest due to content being too difficult or too easy, and improves knowledge absorption efficiency.

[0043] In this invention, the multimodal content generation module also includes a speech narrative optimization submodule. This submodule adopts a youth-friendly speech synthesis engine based on the Transformer architecture, with five built-in basic timbres (children's voice, teenagers' voice, young adults' voice, female voice, and male voice) and three style modes (narrative, interactive, and explanatory). It supports customizable adjustment of speech rate (60-150 words / minute), intonation (0.8-1.2 times the baseline intonation), and volume (50-100%). The speech content generation is optimized for the language habits of teenagers: for younger children (6-9 years old), short sentences (≤10 characters), common vocabulary, and onomatopoeia (such as "the bugle call sounds") are used, avoiding obscure terms; for older children (14-18 years old), written language and logical connectors (such as "in summary" and "therefore it can be seen") are added, and professional expressions are appropriately introduced. A dialect speech optimization library is constructed, correcting the pronunciation characteristics of 20 dialects such as Cantonese, Sichuanese, and Shanghainese. The dialect speech recognition accuracy is ≥92%, and the synthesis accuracy is ≥95%. It achieves contextualized adaptive voice tone (speech rate 130-150 words / minute, tone 1.1-1.2 times, interspersed with ambient sound effects); when narrating a person's family letter, it switches to "warm mode" (speech rate 70-90 words / minute, tone 0.9-1.0 times, with gentle piano music in the background); when conducting Q&A interaction, it switches to "friendly mode" (speech rate 100-120 words / minute, with natural tone fluctuations). It has multi-turn voice dialogue and historical fact correction capabilities: it supports students to ask follow-up questions, and the engine calls the cultural knowledge base to generate coherent answers in real time; after recognizing historical fact errors in the voice narrative (such as confusion of time, people, and events), it immediately triggers double verification, with a correction response time ≤500ms, ensuring the accuracy and rigor of cultural knowledge transmission.

[0044] In this invention, the data storage and analysis module also includes a learning effect visualization submodule. This submodule transforms the analysis results into concrete charts and incentive systems that are easy for teenagers to understand. It has three core functions: First, visualization of knowledge mastery, using a dynamic "growth tree" model. Each knowledge point corresponds to a leaf: ≥80% is a "green leaf," 60%-79% is a "yellow leaf," and <60% is a "withered leaf." Clicking on a leaf allows viewing the learning record and supplementary learning content for the corresponding knowledge point. Second, interest preference analysis, using an "interest radar chart" to display the intensity of preferences (0-5 stars) across eight dimensions, including historical events, biographies, military equipment, and document interpretation, and recommending similar content based on preferences. Third, learning trajectory tracking, using a "timeline" to mark key learning nodes, supporting filtering by time to view corresponding content and interaction data. A complementary "achievement badge system" is included, with various badges awarded for conditions such as "7 consecutive days of learning," "perfect score on knowledge point tests," and "completion of customized storyline tasks." Unlocking a badge triggers an animated explanation, enhancing the sense of accomplishment in learning. The design incorporates multiple viewpoints: the student side displays complete visual content and badges; the parent side provides a simplified report, including weekly learning time (differentiating between VR / AR / touch interaction methods), key and weak knowledge points, and personalized learning suggestions; the teacher side provides a class summary view, anonymously displaying class knowledge mastery rankings and common weak knowledge points, and supports exporting PDF reports for school archiving, facilitating collaborative education between home, school, and school.

[0045] In this invention, the user interaction module also includes a haptic feedback optimization submodule. This submodule, designed for interaction with historical props in VR scenes, utilizes the vibration motor of the force feedback controller (supporting 8 vibration frequency levels and 10 intensity levels) and a position sensor (accuracy ±1mm) to optimize haptic feedback. The calculation of force feedback intensity is performed, where F is the force feedback intensity of the controller (levels 0-100), k is the scene emotion coefficient (1.2 for historical scenes, 0.8 for daily work scenes, and 1.0 for commemorative scenes), s is the interaction displacement (unit: cm), P(x) is the virtual prop hardness function at displacement x (1.0-1.2 for hard objects such as rifles and helmets, 0.3-0.5 for soft objects such as cloth and letters, and 0.6-0.9 for medium hardness such as wooden briefcases), and T(x) is the interaction time function (1.5 for instantaneous touch, 0.8 for continuous grip, and 1.0 for sliding operation). A virtual prop material library is constructed, containing P(x) parameter models for 10 common historical prop materials such as metal, wood, paper, cloth, and ceramics. It supports users to customize the upper limit of feedback intensity (parents can limit the feedback intensity of users aged 6-9 to within level 50 to avoid discomfort from excessive vibration). Achieving haptic and multimodal interaction: When touching the virtual rifle, the controller provides instantaneous strong vibration (level 80-90), simultaneously triggering voice narration, and the VR screen magnifies to show rifle details (such as the magazine and barrel); when flipping through a virtual letter, the controller provides gentle continuous vibration (level 20-30), accompanied by the sound effect of turning pages, and the screen displays magnified text; when holding a virtual red flag, it provides gradual vibration feedback (increasing from level 30 to level 60), simultaneously playing the sound effect of the red flag waving. The feedback logic is optimized for younger users, simplifying vibration modes (retaining only 3 basic intensities), and adding haptic guidance functions (such as indicating the "next click direction" through vibration direction), enhancing the realism and guidance of the interaction, and helping teenagers more intuitively understand the characteristics and usage scenarios of objects in historical scenes.

[0046] In this invention, the security management module also includes a usage time management submodule. This submodule supports multiple roles (parents, homeroom teachers, school administrators) in setting usage rules, which are further divided into basic management and personalized management: basic management includes a single usage time limit (adjustable from 30-120 minutes, default 60 minutes), daily usage time slots (1-3 time slots can be set, such as 19:00-20:00, Saturday 10:00-11:30), and a weekly cumulative time limit (adjustable from 120-300 minutes); personalized management allows setting permissions according to content type. It combines intelligent reminders with mandatory management: when the remaining time is ≤5 minutes, a dual reminder is given via voice ("Time is running out, we suggest you rest after completing the current task") and visual reminder (countdown animation in the corner of the screen); after the timeout, the interactive function is automatically locked (data storage and progress are preserved), and unlocking requires verification of the parent's fingerprint (supports capacitive fingerprint recognition, response time ≤1s) or the teacher's password (6-digit number + letter combination, supports reset). Enhanced usage monitoring and health recommendations: The VR headset camera monitors user posture, issuing reminders when users are hunched over; it tracks continuous usage time, forcibly displaying a 5-minute break every 40 minutes, playing eye exercise animations or historical quizzes (non-interactive). A tiered access control system is implemented: Parents can view all their children's usage data and modify control rules; homeroom teachers can only view the usage time and learning progress of students in their class and modify unified class control rules (requiring school administrator approval); school administrators can view school-wide data and set universal control templates, complying with the "Regulations on the Protection of Minors Online" and fulfilling their responsibility for managing minors' online use.

[0047] In this invention, the system operation and maintenance module also includes a load balancing and intelligent expansion sub-module. This sub-module adopts a distributed node architecture (containing 10 core server nodes and 5 backup nodes), and monitors the CPU utilization (warning threshold 80%), memory usage (warning threshold 85%), disk I / O rate (warning threshold 100MB / s), and concurrent connection count (warning threshold 1000) of each node in real time. It allocates user requests by improving the round-robin algorithm (combining node load weight, with higher weight for lower load) and the request response time is ≤100ms. Predictive load scheduling and intelligent scaling are achieved: A load prediction model is built based on historical data (peak usage periods of the past 6 months, such as 15:30-17:00 Monday to Friday and 9:00-11:00 on holidays). Idle nodes are switched to active status 10 minutes in advance. When the predicted concurrency exceeds 120% of the current node's capacity, an elastic cloud node is automatically started (response time ≤ 3 minutes, single node supports 500 concurrent requests). During peak periods (concurrency exceeding 3 times the weekday level), a traffic shaping mechanism is activated to prioritize interactive requests (such as VR scene loading and voice Q&A) and delay non-core requests (such as historical data backup). Fault self-healing and data consistency are guaranteed: A heartbeat detection mechanism is adopted (a detection packet is sent every 2 seconds). When a node failure is detected, user requests for that node are automatically migrated to a backup node (migration time ≤ 200ms, imperceptible to users). At the same time, data backup is triggered (incremental backup frequency 5 minutes, full backup is performed every morning). Distributed locks and a data synchronization protocol (Raft algorithm) are used to ensure data consistency across multiple nodes and avoid progress loss or data corruption caused by request migration. The system is designed with an energy-saving operation and maintenance mode: During off-peak periods (e.g., 0:00-6:00 AM, with a concurrency of <100), 50% of core nodes and all standby nodes are automatically shut down, and a sleep-wake mechanism is enabled (waking up within 100ms upon receiving a request); node resource utilization is monitored, and nodes with utilization of <30% for one consecutive hour are run at reduced frequency to reduce system energy consumption. A supporting visual operation and maintenance backend is provided, displaying node status, load distribution, fault statistics, and expansion records. Remote operation and maintenance operations (such as node restart, configuration modification, and software update) are supported, with a fault repair success rate of ≥90%. Version updates adopt a canary release mode (updating two core nodes first, and then performing a full update after testing without issues, executed during non-teaching periods) to ensure stable 24 / 7 operation of the platform and meet the needs of a large number of teenagers using it online simultaneously.

[0048] Example 1 "Introduction to Historical Figures and Their Deeds" Experience for Third Graders (Scenario for Younger Children Aged 6-9) This embodiment addresses the "historical events enlightenment" teaching needs of third grade (4 classes, 160 students, aged 6-9, preoperational cognitive stage) in a certain city's experimental primary school. Traditional classrooms rely solely on picture explanations, resulting in students' attention span of less than 15 minutes and a knowledge retention rate of less than 40%. This invention platform aims to achieve fun and engaging enlightenment, and the specific implementation is as follows.

[0049] 1. System Deployment and Parameter Configuration 2. The user interaction module is equipped with a Pico4CVR headset (4K resolution, 110° field of view, 295g weight, suitable for young children), a 21.5-inch touchscreen (45ms response time), and an iFlytek AI speaker (supports 20 dialects, 80ms wake-up time). The force feedback controller limits vibration intensity to ≤50 levels. The wearable physiological sensor uses the Xiaomi Mi Band 8 Youth Edition (45g weight, 10-hour battery life, heart rate measurement 60-180 beats / min, skin conductance 0-5μS), connected to the platform via Bluetooth 5.0. The cultural resource library uses a subset of events from the "Historical Events Library," including 5 3D scenes, 20 cartoon images, 10 3-minute audio stories, and 15 interactive tasks (puzzle, coloring, voice Q&A). The multimodal content generation module uses the GPT-4 Mini model and the StableDiffusionLite engine, with text vocabulary limited to the second-grade curriculum standards for compulsory education, and sentence length ≤8 characters. The intelligent adaptation module includes an entrance test, categorizing users into three levels based on their basic cognitive abilities. The security control module has rules set by the homeroom teacher: single use ≤40 minutes, open daily from 16:30-17:10, and violent content disabled (replaced with cartoon narrative). The system operation and maintenance module uses 8 core nodes, predicting a concurrent load of 160 at 16:30, and activates all nodes 10 minutes in advance.

[0050] 2. Implementation details of core processes 2.1 Profile Creation and Content Adaptation: Students register via touchscreen upon their first login, inputting their age as 7 years old and completing an entrance test (70% accuracy). The intelligent adaptation module generates a profile: age 6-9 years, early cognitive development, interests ("person stories"), and interaction habits ("voice + touch"). The cognitive development matching submodule calculates the cognitive matching coefficient. The data consists of n=8 historical pre-stored data entries, d_i is 0.2-0.3 (e.g., d=0.2 for "identifying a person's name"), L_i is 2-3 minutes, f(L_i)=0.7-0.8, Ki=60%-80%, g(K_i)=0.84-0.92, and C=0.82≥0.7 is obtained. The "Person's Deeds Enlightenment Package" is then pushed.

[0051] 2.2 Multimodal Interaction Experience The emotion perception and adaptation submodule collects data in real time: When a student wears sensors to view a VR scene, their heart rate increases from 80 to 105 beats per minute, their skin conductance increases from 0.8 μS to 1.5 μS, the VR headset camera captures micro-expressions of "awe," and their voice remains calm. Emotion matching degree is calculated. T=180s, H(t) normalized to 0.6-0.8, S(t) 0.3-0.7, V(t) 0.5-0.6, O(t) 0.8-0.9, solving for M=0.78≥0.8, the scene is extended to 4 minutes, triggering a detailed explanation.

[0052] AR desktop fusion mode activated: Students scan textbook page numbers with tablets, and a 3D human statue (500,000 polygons) is overlaid on the screen. They can zoom to 15cm in height with two fingers, touch the statue to trigger voice commands, and the ambient light adaptation function adjusts the statue's brightness to 120cd / m² based on the classroom lighting (400 lux). 2 The shadow matches the desktop texture.

[0053] Touchscreen interaction: Complete the "Character Puzzle" task (3 clues), ask and answer questions via voice interaction, students answer in Sichuan dialect, the voice narrative optimization sub-module recognizes and responds in a child's voice, and a cartoon map is displayed on the touchscreen simultaneously.

[0054] 2.3 Effectiveness Evaluation and Safety Management: The learning effectiveness visualization submodule generates a "growth tree": 8 knowledge points unlock 6 green leaves and 2 yellow leaves, unlock badges, and provide a 30-second animated explanation of the event. Parents receive a report: After 25 minutes of learning, students have mastered the "person's name" and "main events," and it is recommended to complete the "historical event time" coloring task.

[0055] Safety management triggers an alert: When 5 minutes remain, a voice prompt says "Time to rest," and the touchscreen displays a countdown. The device automatically locks after the timeout period, requiring a parent's fingerprint to unlock. The system maintenance module monitors node CPU utilization at 65% and memory usage at 70%. The load balancing submodule distributes requests using a round-robin algorithm, with a response latency of 90ms.

[0056] 3. Performance data representation Table 1: Comparison of teaching effectiveness between traditional classrooms and the platform of this invention for younger children index Traditional classroom This invention system Average attention span 12 minutes 32 minutes Core knowledge point retention rate 38% 82% Learning interest rating (1-5) 2.1 points 4.6 points Emotional resonance incidence 15% 78% Parent satisfaction 65% 94% Table 1 shows data from a comparative test of four classes. Traditional classrooms rely solely on image-based explanations, leading to easily distracted young students with a concentration time of only 12 minutes, a knowledge retention rate of less than 40%, and low emotional resonance. This invention dynamically adjusts the content pace through emotional perception adaptation, enhances interest through AR / VR multimodal interaction, and lowers the operational threshold with voice and touch controls, extending concentration time to 32 minutes and increasing the retention rate to 82%. The emotional resonance rate reaches 78%, reflecting the platform's upgrade from "knowledge transmission" to "emotional cultivation." Parent satisfaction with time management and content suitability reaches 94%, meeting the teaching needs of young children.

[0057] Example 2 A "Literature Interpretation" Experience for Second-Year High School Students (Scenario for 16-18 Year Olds) This embodiment addresses the teaching needs of "Historical Document Interpretation and Decision Simulation" for second-year students (3 classes, 120 students, aged 16-18, in the formal operational cognitive stage) at a key high school. Traditionally, relying solely on text reading results in insufficient student comprehension depth, with an accuracy rate of less than 50% for analytical questions. This invention's platform enables in-depth interaction, and the specific implementation is as follows.

[0058] 1. System Deployment and Parameter Configuration: The user interaction module is configured with an HTC Vive XRElite VR headset (4K resolution, 110° field of view), a 27-inch touchscreen (30ms response time), and a Baidu smart speaker (supporting multi-turn dialogue). The force feedback controller vibration intensity is adjustable from 0-100 levels. The physiological sensor used is the Huawei WatchGTRunner (measuring heart rate and blood oxygen, 14-hour battery life). The cultural resource library utilizes a subset of historical documents from the "Historical Document Library," including 10 3D scenes, 50 pages of scanned documents (300 DPI), 8 expert interpretation audio clips, and 20 analysis tasks. The multimodal content generation module uses a lightweight GPT-4 model; the text contains logical connectors, and the voice uses a youthful, explanation-style tone. The intelligent adaptation module includes an entrance test (10 analysis questions, 75% accuracy rate) to categorize users into advanced cognitive levels. Teachers create historical document scenarios through the interactive scenario customization sub-module: those with a correct answer rate of ≥85% enter the "Strategy Analysis" branch, and those with a rate <85% enter the "Historical Fact Consolidation" branch. The evaluation is structured with 40% process-based assessment and 60% outcome-based assessment. The system operation and maintenance module uses 10 core nodes, predicting a concurrent load of 120 at 15:30, and activates 2 backup nodes in advance. The load warning thresholds are 80% for CPU and 85% for memory.

[0059] 2. Implementation details of core processes 2.1 Storyline Customization and Image Adaptation Teachers log into the web editor and create a storyline based on a "historical framework": Node 1 "Reading Excerpts from Documents," Node 2 "Three-Stage Analysis of Historical Events" (unlocking "Strategy Simulation" with a correct answer rate ≥85%), and Node 3 "Predicting the Outcome of Historical Events." Local "oral recordings" are added as clues, and two hints are set. The storyline is reviewed and approved within 10 minutes, generating a QR code. Students scan the code to log in, and the intelligent adaptation module generates a profile: 17 years old, advanced cognitive level, interest in "strategy analysis," and interaction habit "VR + voice." The cognitive development matching submodule calculates coefficients. n=12 historical data points =0.7-0.9 (e.g., "Strategic Significance Analysis" d=0.9), =5-8 minutes, =0.6-0.7, =70%-85%, =0.78-0.9, solving for C, we get C=0.75≥0.7, and push the "Strategy Analysis Branch" content.

[0060] 2.2 Deep Interaction and Experience Optimization in VR Immersion: Entering the "Cave Scene," touching a replica of a historical document on a virtual desk; the haptic feedback submodule calculates the intensity. Given k=1.0 (daily scenario), s=8cm, P(x)=0.4 (paper hardness), T(x)=1.5 (instantaneous touch), we obtain F=48 levels. The controller provides gentle vibration feedback, synchronized voice explanation, and the screen displays detailed annotations on the document. Multimodal content generation: When a student asks a question, the voice narrative optimization submodule responds with a youthful voice and a speech rate of 130 words / minute, including logical connectors like "firstly" and "secondly," and simultaneously generates a three-stage timeline (scalable) on the touchscreen. The system recognizes a student's mistaken statement "the three stages are 1937-1945," triggering historical fact correction and connecting to the historical database to correct it to "the three stages are before autumn 1938, 1938-1943, and 1943-1945," with a response time of 450ms. AR Mobile Integration: Students activate mobile mode in the campus square, scan the ground to generate a 3D scene (including terrain and personnel deployment), adjust the viewpoint with gestures, trigger the "Strategy Development" task, select an "Event," and display the result simulation animation. The ambient light adaptation function adjusts the brightness to 200 cd / m² based on the outdoor light intensity (8000 lux). 2 .

[0061] 2.3 Effectiveness Evaluation and System Operation and Maintenance: Visualization of Learning Outcomes: The "Growth Tree" displays 12 knowledge points with 10 green leaves; the "Strategy Analysis" section on the interest radar chart is rated 4.5 stars; the timeline marks the "Completed Decision Simulation" node, unlocking the "Strategy Analyst" badge. The teacher's end displays a class report: accuracy rate 82%, weakness "Impact of International Situation," with recommended supplementary expert interpretation. System Operation and Maintenance: At 15:35, concurrency reached 120, CPU utilization was 78%, memory usage was 82%, and the load balancing submodule distributed requests according to weights; at 15:40, a sudden increase of 20 students triggered elastic scaling, cloud nodes were started within 3 minutes, load dropped to 70%, and response latency was 95ms. At 16:00, a node failed; after heartbeat detection, requests were migrated within 200ms without student awareness.

[0062] 3. Performance data representation Table 2: Comparison of Teaching Effects of Traditional Text-Based Teaching and the Invention Platform for Older Children index Traditional text teaching This invention system Literature Comprehension Depth Score 3.2 points (1-5) 4.7 points (1-5) Accuracy rate of analytical questions 45% 82% Active exploration time 5 minutes 28 minutes Completeness of viewpoint expression 40% 85% Teachers' lesson preparation efficiency 4 hours / class 1.5 hours / class Table 2 shows data from a comparative test of three classes. Traditionally, relying solely on text reading, older students struggle to grasp abstract strategic concepts, achieving only a 45% accuracy rate on analytical questions and exhibiting low initiative in inquiry. This invention uses VR immersion and AR strategy simulation to concretize abstract content. A customized storyline submodule reduces teacher preparation costs, and multi-round voice Q&A meets inquiry needs, resulting in a comprehension depth score of 4.7 and an accuracy rate of 82%. Initiated inquiry time was extended to 28 minutes, with 85% completeness of viewpoint expression, demonstrating the platform's suitability for older students' logical thinking and inquiry abilities. The system's flexible scalability and self-healing capabilities ensure continuous teaching and meet the needs of in-depth high school instruction.

[0063] Figure 2 This invention intuitively demonstrates the core value of the emotional perception and adaptation sub-module. Traditional text-based interactions rely solely on visual input, resulting in an emotional matching score generally below 0.4, failing to evoke emotional resonance in teenagers. While standalone VR offers immersion, it lacks a dynamic adjustment mechanism, achieving a matching score of only around 0.6. This invention uses a wristband sensor to collect heart rate and electrodermal signals, combined with a VR headset to capture micro-expressions. An integral formula is used to calculate the emotional matching score, dynamically adjusting the content's rhythm and form, resulting in a matching score exceeding 0.8 for all age groups, far surpassing the emotional resonance threshold. This verifies the role of multimodal emotional interaction in promoting the "entering the mind and heart" of culture, allowing teenagers to deepen their understanding from the cognitive level to emotional identification.

[0064] Figure 3This invention clearly reflects the optimization effect of the cognitive development matching submodule. Traditional solutions use uniform content delivery, with the adaptation rate only increasing from 0.4 to 0.65 as cognitive stages progress. This results in insufficient adaptation for younger children due to overly difficult content, and a lack of value for older children due to overly simplistic content. Based on Piaget's theory, this invention divides cognitive stages into six phases and uses the C-value formula to integrate difficulty, duration, and mastery data to accurately match content difficulty. The adaptation rate for each stage remains consistently above 0.75. This solves the cognitive misalignment problem of "one-size-fits-all" content delivery, providing engaging and interactive content for younger children and increasing logical analysis for older children, thus balancing educational value and adaptability.

[0065] Figure 4 This invention highlights the comprehensive benefits of multi-module collaborative innovation. Traditional classrooms are weak in all dimensions, with only 20% of students showing initiative in inquiry and less than 20% experiencing emotional resonance, making it difficult to achieve in-depth education. This invention constructs an immersive learning scenario through VR / AR multimodal interaction (user interaction module), emotional dynamic adaptation (emotional perception sub-module), voice narrative optimization (content generation module), and haptic feedback (interaction optimization sub-module), achieving breakthroughs of over 75% in attention and retention rates. This verifies the platform's transformation from "one-way instruction" to "active participation," elevating cultural learning from knowledge memorization to ability cultivation and emotional shaping.

[0066] Figure 5 This invention visually demonstrates the advantages of the load balancing and intelligent scaling submodules. Traditional systems lack load scheduling capabilities; concurrency exceeding 200 users triggers a lag threshold, and at 500 users, latency reaches 800ms, leading to crashes. This invention employs an improved round-robin algorithm to allocate requests, automatically activating elastic cloud nodes when concurrency reaches 300 users. Through predictive scheduling, resources are activated in advance, consistently maintaining latency below 105ms. This solves the system lag problem during peak usage periods on campus, ensuring continuous and smooth teaching and meeting the needs of large numbers of teenagers online simultaneously.

[0067] Figure 6 The results validate the practical value of the interactive storyline customization submodule. Traditional platforms lack visual editing tools, requiring teachers to manually upload resources and write scripts, resulting in over 4 hours of preparation time and a customization success rate of only 10%, making it difficult to adapt to personalized teaching. This invention provides a storyline branch editor, template library, and evaluation configuration functions, allowing teachers to quickly customize task nodes and scoring rules, reducing preparation time to 1.5 hours and achieving a customization success rate of 95%. Classroom adaptation satisfaction and teaching effectiveness have significantly increased, demonstrating the platform's deep adaptation to teaching scenarios and providing teachers with flexible and efficient educational tools.

[0068] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A multi-modal interactive experience platform for youth education, characterized in that, include: The user interaction module includes an immersive interaction unit, a touch interaction unit, and a voice interaction unit. The immersive interaction unit is equipped with a VR headset and force feedback controllers. The touch interaction unit uses a multi-touch screen. The voice interaction unit supports voiceprint recognition for teenagers, receives commands in Mandarin Chinese and 20 dialects, and supports voice wake-up function. The cultural resource library module is divided into historical scene library, historical event library, document library and interactive task library according to content. The resources are updated quarterly and are subject to three levels of review. The multimodal content generation module uses the GPT-4 lightweight model and the StableDiffusion image generation engine to generate text, 3D scenes, and interactive scripts that are adapted to the cognitive abilities of teenagers. It supports multi-format output and the content has been pre-verified by experts. The intelligent adaptation module calls resources and generates module content based on user profiles, and adapts and adjusts response time and content complexity accordingly; The data storage and analysis module uses distributed storage to store user interaction logs, profiles, and generated content. It uses big data algorithms to mine learning paths, knowledge mastery, and interaction preferences, and outputs weekly learning reports. The security management module includes a content security unit and a privacy protection unit. It uses hash encryption to de-identify and store sensitive user data, and assigns permissions based on the "minimum necessary" principle. System maintenance module: Monitors hardware status and software performance 24 / 7. Fault warning threshold is hardware failure rate > 1% / response delay > 200ms. Dispatch work order within 10 minutes after warning. Supports remote maintenance and automatic updates during non-teaching hours.

2. The multimodal interactive experience platform for youth education according to claim 1, characterized in that, Also included is an emotion perception and adaptation sub-module, which integrates a physiological sensor and an emotion analysis engine. The physiological sensor adopts a wearable wristband design to collect the user's heart rate, galvanic skin response, and blood oxygen saturation. The emotion analysis engine combines voice emotion features and facial micro-expression data to calculate the user's real-time emotion matching degree according to the following formula: M = aH(t) + bS(t) + cV(t) + dO(t) T where M is the emotion matching degree, T is the interaction time length, H(t) is the normalized value of the heart rate at time t, S(t) is the normalized value of the galvanic skin response at time t, V(t) is the normalized value of the voice emotion at time t, O(t) is the normalized value of the facial micro-expression at time t, and a, b, c, and d are weight coefficients of the heart rate, galvanic skin response, voice, and facial expression, respectively.

3. The multi-modal interactive experience platform for youth education of claim 1, wherein, It also includes an AR scene virtual-real fusion submodule, which uses visual SLAM+IMU tightly coupled positioning technology and deep learning image recognition algorithm to overlay cultural virtual elements onto real scenes, adapting to multiple devices; it has three fusion modes; it supports ambient light adaptation and shadow rendering; it can trigger explanations through gestures; and teachers can customize virtual element parameters and trigger conditions through the web interface.

4. The multi-modal interactive experience platform for youth education of claim 1, wherein, The cultural resource library module also includes an interactive story customization sub-module, which provides a visual story branch logic editor. Teachers can customize the story based on the historical framework, add conditional task nodes, and configure multiple types of interactive forms. It supports uploading local multimedia materials, adding custom narration, and setting 0-3 level clue prompts; it constructs a graded evaluation system for process and outcome; after customized plots are reviewed for compliance, a QR code and sharing link are generated. Students can scan the code / click to enter, and the data is synchronized to the teacher's end in real time. It supports viewing student progress, class completion rate, the top 3 knowledge points with the highest error rate, generating tutoring suggestions, and providing 6 sets of plot templates for different grade levels.

5. The multimodal interactive experience platform for youth education according to claim 1, characterized in that, The intelligent adaptation module further comprises a cognitive development matching sub-module. Based on the Piaget cognitive development theory and the youth cultural learning outline, the cognitive level is refined into six stages. According to the historical learning data of the user, the historical learning data of the user is combined with the learning content to calculate the cognitive matching coefficient of the learning content. A content cognitive matching coefficient is calculated, wherein C is the cognitive matching coefficient, n is the number of historical learning content, d_i is the difficulty coefficient of the i-th content, f(L_i) is the learning time function of the i-th content, L_i is the learning time in minutes, g(K_i) is the knowledge mastery function of the i-th content, and K_i is the test accuracy rate corresponding to the content.

6. The multi-modal interactive experience platform for youth education of claim 1, wherein, The multimodal content generation module also includes a speech narrative optimization submodule: it adopts a Transformer architecture speech synthesis engine; it uses short sentences of ≤10 characters and onomatopoeia for younger children, and adds written language and logical connectors for older children; it builds a library of 20 dialects for optimization; and it features contextualized adaptive intonation. It supports multi-turn voice dialogue and triggers double verification after recognizing historical errors.

7. The multi-modal interactive experience platform for youth education of claim 1, wherein, The data storage and analysis module also includes a learning effect visualization sub-module, which transforms the analysis results into growth trees, interest radar charts, and timelines; It includes a system of 30 achievement badges; set up Multi-role view: The student side displays visual content and badges, the parent side displays weekly learning time, strong and weak knowledge points and suggestions, and the teacher side displays class summary data and common weaknesses, and supports exporting PDF reports.

8. The multimodal interactive experience platform for youth education according to claim 1, characterized in that, The user interaction module also includes a haptic feedback optimization submodule. This submodule is designed for interacting with historical props in VR scenes, utilizing the vibration motor and position sensor of a force feedback controller to provide haptic feedback. The calculation of force feedback intensity is given by F, where F is the force feedback intensity of the controller, k is the scene emotion coefficient, s is the interaction displacement, P(x) is the virtual prop hardness function at displacement x, and T(x) is the interaction time function.

9. The multimodal interactive experience platform for youth education according to claim 5, characterized in that, The safety management module also includes a usage time management sub-module: it supports parents / class teachers / school administrators in setting rules, with basic management including single usage time, daily time slots, and weekly cumulative usage, and personalized management by setting permissions according to content type; it provides voice and visual reminders when the remaining time is ≤5 minutes, and automatically locks the device after the timeout; it monitors posture and provides reminders, and forces a 5-minute break every 40 minutes; and it allocates data viewing and rule modification permissions according to the hierarchy of parents-class teachers-school administrators.

10. The multimodal interactive experience platform for youth education according to claim 4, characterized in that, The system operation and maintenance module also includes a load balancing and intelligent expansion sub-module, which adopts a distributed node architecture, monitors nodes in real time and allocates requests through an improved round-robin algorithm; builds a load prediction model based on historical data, activates idle nodes 10 minutes in advance, and automatically starts elastic cloud nodes when the predicted concurrency exceeds 120% of the load. During peak periods, a traffic shaping mechanism is activated to ensure data consistency. It comes with a visual backend for operation and maintenance, supporting remote operation.

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